Founder & CEO
Founder Bharath Gaddam had a clear diagnosis: the problem wasn't data or talent, it was architecture. Correlation-based models were never going to cut it for the complexity of enterprise growth. The industry wasn't under-resourced. It was fundamentally mis-built.
See how Fortune 500 retailers use AI for demand forecasting, personalization, and supply chain, and where most retail AI investment still falls short of the P&L.
Predictive analytics tells you what will happen; causal AI tells you what to do about it. The methodology difference, and when each one fits.
AI agents execute marketing decisions at scale. But can they own them? Why LLM agents recommend but don't decide, and what causal reasoning changes.
Unified marketing measurement usually means combining MMM, attribution, and incrementality. But that's still fragmentation. Here's why we say so, and what consolidating everything into one causal model changes.
We define the Large Causal Model, and how it differs from three academic uses of the term and from a standard large language model.
Consolidating data into one warehouse does not stop teams getting different answers. The real fragmentation is in the models. Here is how to fix it.
A step-by-step way to build the AI business case your CFO will fund: the spend table, the objections, and the build-vs-buy call.
Most enterprise AI projects fail for reasons no better model can fix. Here is the architecture problem underneath, and how to solve it.
A practical guide to AI for business growth for enterprise leaders — the use cases that move the P&L, why most underdeliver, and what connects them into one decision model.
Enterprise planning unifies strategy and execution. See how Fortune 500 leaders build one plan across finance, supply chain, and commercial.
Causal AI explains why outcomes happen, not just what correlates. A plain-English guide to how it works, what it replaces, and where it adds value.
Most enterprises only plan tactically. See how tactical, functional, and strategic decisions connect into one growth-planning loop inside POEM365.
CRM unified customer data. ERP unified operations. Enterprise Decision AI is the fourth category, unifying the enterprise growth decision into one model.
MMM, attribution and forecasting all report what happened. None tell you what to do next. How Enterprise Decision AI prescribes where the next dollar goes.
Traditional MMM reports only direct channel impact. See the three layers of channel ROI, direct, synergistic and long-term equity, that your budget misses.
A Large Causal Model decides; an LLM describes. Here is how they differ, where they fit, and why your AI shortlist probably only has half the picture.
Enterprise analytics has optimized based on correlation for 40 years. Causal architecture finally produces one unified growth answer.
Most AI agents retrieve and summarize. POEM365's four causal Decision Agents forecast, plan, optimize and explain enterprise growth. See how they work.
Every function can hit its KPI while the business loses money, because no model sees the system. Learn why the overlap goes unpriced, and how to fix it.
How a Fortune 500 brand recovered $130M in growth from the same marketing budget, using POEM365's unified causal model. Same spend, very different result.
Let's talk about how we can help you grow your business.